Field of the Subject Disclosure
The present subject disclosure relates to spectral unmixing in digitized brightfield and fluorescence microscopy. More particularly, the present subject disclosure relates to accelerating the spectral unmixing process by identifying groups of similar pixels and spectrally unmixing similar pixels together.
Background of the Subject Disclosure
In a multiplex slide of a tissue specimen, different nuclei and tissue structures are simultaneously stained with specific biomarker-specific stains, which can be either chromogenic or fluorescent dyes, each of which has a distinct spectral signature, in terms of spectral shape and spread. The spectral signatures of different biomarkers can be either broad or narrow spectral banded and spectrally overlap. A slide containing a specimen, for example an oncology specimen, stained with some combination of dyes is imaged using a multi-spectral imaging system. Each channel image corresponds to a spectral band. The multi-spectral image stack produced by the imaging system is therefore a mixture of the underlying component biomarker expressions, which, in some instances, may be co-localized. More recently, quantum dots are widely used in immunofluorescence staining for the biomarkers of interest due to their intense and stable fluorescence.
Identifying the individual constituent stains for the biomarkers and the proportions they appear in the mixture is a fundamental challenge that is solved using a spectral unmixing operation. Spectral unmixing decomposes each pixel of the multi-spectral image into a collection of constituent spectrum end members or components, and the fractions of their intensity contributions in the multi-spectral image from each of them. An example spectral unmixing method is a non-negative linear least squares operation commonly used both in fluorescent and brightfield microscopy. This operation is typically performed on every pixel of an image, one at a time.
The publication ‘Adaptive Spectral Unmixing for Histopathology Fluorescent Images’ by Ting Chen et al, Ventana Medical Systems, Inc. provides an introduction and an overview as to various prior art techniques for spectral unmixing of multiplex slides of biological tissue samples, the entirety of which is herein incorporated by reference. Various other techniques for spectral unmixing of tissue images are known from WO 2012/152693 A1 and WO 2014/140219 A1.